Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917934743420928 |
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| author | Shi, Jiatong Chen, William Berrebbi, Dan Wang, Hsiu-Hsuan Huang, Wei-Ping Hu, En-Pei Chuang, Ho-Lam Chang, Xuankai Tang, Yuxun Li, Shang-Wen Mohamed, Abdelrahman Lee, Hung-yi Watanabe, Shinji |
| author_facet | Shi, Jiatong Chen, William Berrebbi, Dan Wang, Hsiu-Hsuan Huang, Wei-Ping Hu, En-Pei Chuang, Ho-Lam Chang, Xuankai Tang, Yuxun Li, Shang-Wen Mohamed, Abdelrahman Lee, Hung-yi Watanabe, Shinji |
| contents | The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification. The challenge comprises a research track focused on applying ML-SUPERB to specific multilingual subjects, a Challenge Track for model submissions, and a New Language Track where language resource researchers can contribute and evaluate their low-resource language data in the context of the latest progress in multilingual speech recognition. The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages. The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks, and a variety of speech/voice types present significant challenges in multilingual speech processing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_05513 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond Shi, Jiatong Chen, William Berrebbi, Dan Wang, Hsiu-Hsuan Huang, Wei-Ping Hu, En-Pei Chuang, Ho-Lam Chang, Xuankai Tang, Yuxun Li, Shang-Wen Mohamed, Abdelrahman Lee, Hung-yi Watanabe, Shinji Sound Computation and Language Audio and Speech Processing The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification. The challenge comprises a research track focused on applying ML-SUPERB to specific multilingual subjects, a Challenge Track for model submissions, and a New Language Track where language resource researchers can contribute and evaluate their low-resource language data in the context of the latest progress in multilingual speech recognition. The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages. The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks, and a variety of speech/voice types present significant challenges in multilingual speech processing. |
| title | Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2310.05513 |